AI Personalized Outfit Stylist App

I’m putting together a mobile-first application that acts as a personal stylist and shopping assistant. The single most important capability is an intelligent engine that generates complete outfit combinations for each user. These looks must be driven by the user’s own, self-declared style preferences rather than generic, one-size-fits-all advice.

Core workflow
• A new user sets a budget and fills in their style profile (simple text inputs or any method you recommend).
• Your algorithm analyses that data, calls on fashion catalogues or affiliate feeds, and returns full outfits: clothes, shoes, and accessories that work together.
• Each recommendation includes direct shopping links so the user can purchase immediately.

I already have the high-level concept and feature list; what I need from you is the technical realisation—end-to-end. That means UX wireframes, the recommendation/AI layer, catalog integration, and a lightweight backend that can scale later. Preferred stack is completely open; use the tools you are most productive with (Python, Node, React Native, Swift, Firebase, or similar are all fine).

Deliverables
1. Clickable prototype that shows the onboarding flow, the style-preference capture, and the outfit feed.
2. Working MVP with:
– Outfit combination engine tied to personalized style preferences
– Budget filter and affiliate/shopping link integration
– Simple admin panel to upload or sync new catalogue items
3. Short technical hand-off document and recorded demo walk-through.

Acceptance criteria
• Outfits feel cohesive (no clashing colours or duplicate items).
• Response time from preference submission to first suggestions ≤ 5 seconds on mid-tier devices.
• Codebase is clean, commented, and ready for a hand-off to an in-house team.

If this sounds like your arena, tell me which existing computer-vision or recommendation frameworks you’d lean on and roughly how long the MVP would take you.